How Does Numpy Random Seed Changes?

So, I'm in a project that uses Monte Carlo Method and I was studying the importance of the seed for pseudo-random numbers generation.

While doing experiments with python numpy random, I was trying to understand how the change in the seed affects the randomness, but I found something peculiar, at least for me. Using numpy.random.get_state() I saw that every time I run the script the seed starts different, changes once, but then keeps the same value for the entire script, as show in this code where it compares the state from two consecutive sampling:

import numpy as np

rand_state = [0]
for i in range(5):
    rand_state_i = np.random.get_state()[1]
    # printing only 3 state numbers, but comparing all of them
    print(np.random.rand(), rand_state_i[:3], all(rand_state_i==rand_state))
    rand_state = rand_state_i

# Print:
# 0.9721364306537633 [2147483648 2240777606 2786125948] False
# 0.0470329351113805 [3868808884  608863200 2913530561] False
# 0.4471038484385019 [3868808884  608863200 2913530561] True
# 0.2690477632739811 [3868808884  608863200 2913530561] True
# 0.7279016433547768 [3868808884  608863200 2913530561] True

So, my question is: how is the seed keeping the same value but returning different random values for each sampling? Does numpy uses other or more "data" to generate random numbers other than those present in numpy.random.get_state()?